A Multi-Objective Situational Awareness Approach for Distribution Networks Using Drones

Unmanned aerial vehicles (drones) have gained a high level of practical confidence due to their ease of use in different applications as well as their advantages such as flexibility, ease of operation and low cost. In this paper, we propose the use of drones equipped with onboard sensors for monitor...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Shahin, Haiam, Shaaban, Mostafa F., Ismail, Mahmoud H., Mourad, Hebat-Allah M., Khattab, Ahmed
Format: Article
Language:English
Subjects:
Costs
Distribution network
Distribution networks
Drone aircraft
Drone vehicles
Drones
Genetic algorithms
Monitoring
Multiple objective analysis
Networks
non-dominated sorting genetic algorithms
Observability
Optimization
Pareto optimization
power source sizing
Resource management
Situational awareness
Sorting algorithms
trip planning
Unmanned aerial vehicles
Working hours
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Summary:Unmanned aerial vehicles (drones) have gained a high level of practical confidence due to their ease of use in different applications as well as their advantages such as flexibility, ease of operation and low cost. In this paper, we propose the use of drones equipped with onboard sensors for monitoring the status of electrical distribution networks. Specifically, we formulate a multi-objective optimization approach for distribution networks' situational awareness, which aims to minimize two conflicting objectives. These are the total annual cost and loss of observability. The proposed approach decides the optimal number of drones, their batteries, the nodes to be visited, and the trip plan. In order to obtain a set of Pareto solutions, we utilize the non-dominated sorting genetic algorithm (NDSGA) in conjunction with branch-and-bound algorithm to minimize both objectives. To validate the performance of the proposed approach, we applied it to a practical distribution system under different scenarios of monitoring frequency and working hours. The obtained results prove the effectiveness of the proposed approach.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3267977